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Immuno-oncology Simulator™

Revolutionizing immuno-oncology with predictive modeling

QSP model for immuno-oncology therapies

Certara’s Immuno-oncology (IO) Simulator is a cutting-edge quantitative systems pharmacology (QSP) modeling platform designed to predict optimal drug combinations, dosing regimens, and biomarkers in virtual patients with solid tumors. When monotherapies do not effectively address the complexities of solid tumors, a new approach is needed where combination therapies can be considered. By simulating the complex dynamics of the Cancer Immunity Cycle, the IO Simulator empowers researchers to make data-driven decisions for immuno-oncology therapies. This innovative tool integrates pre-clinical data to refine models and forecast clinical outcomes, enabling the development of personalized treatment strategies for specific indications and tumor phenotypes.

Determine optimal drug combination
Optimize dosing regimens for combination therapies
Predict survival outcomes for solid tumor patients

Features of the IO Simulator immuno-oncology modeling platform

The IO Simulator offers a mechanistic model of the Cancer Immunity Cycle, delivered through QSP Designer software. It facilitates modular development of a compound’s mechanism of action, calibrated with pre-clinical data, and integrates these models to predict clinical outcomes. Virtual trials conducted with the IO Simulator account for tumor heterogeneity, providing insights into optimal dosing and patient stratification biomarkers.

Modules of IO Simulator

  • Tumor, lymph, and blood compartments
  • Tumor cells
  • T cells
  • Dendritic cells
  • Macrophages
  • NK cells
  • Multiple cytokines

Drug modalities

  • mAb
  • bsAb
  • ADC
  • 低分子

Model is calibrated to clinical data from the following drugs

  • Pembrolizumab (anti-PD1)
  • Nivolumab (anti-PD1)
  • Ipilimumab (anti-CTLA4)
  • Sotigalimab (anti-CD40; agonist)
  • Docetaxel (chemotherapy)

Speak to a QSP expert

Ask us how the IO Simulator can build confidence into your immuno-oncology therapies.

Predict optimal drug combinations and dosing regimens
Identify patient stratification biomarkers for personalized therapies
Simulate clinical outcomes with unparalleled precision

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